The knowledge of global features gives the network a better understanding of the object as a whole, e.g., features such density and spacing are model specific.
The PointNet [1]} architecture uses a series of shared MLP with shared weights to extract the global features for classification and segmentation tasks.
We also uses the same network to extract global features.
For a input point cloud \(X_p\)  with shape \(n \times 6\) , the output of global features \(X_g\)  is \(1 \times g\)  where \(g\)  is the number of features extracted in the last MLP layer.
